First Difference Regression

OK, now that I’ve written all that, I guess I can answer better:
I’m more comfortable doing analysis to improve forecasting myself, but ultimately we’re in this business to make money, and so one has to make the trade or construct the portfolio.
But my instinct, when using these kinds of models is to use a shrinkage factor, chosen subjectively, to account for the fact that these models always look more certain than they really are.
 
LPoulin133 Wrote:
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> maratikus - To your point, most literature focuses
> on the latter (as far as ‘complete’ models go),
> rather than the former, though my sense it takes a
> certain level of infrastructure to prove viability
> in practice. Has this been your experience?
I want to make sure I understand your question. Are you talking about spread trading vs error-correction model (improved forecasts)?
 
maratikus Wrote:
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> I want to make sure I understand your question. Are you talking about spread trading vs error-correction model (improved forecasts)?
Yea, since you can spread anything in almost any manner with as much discretion as you want there are many ways to evaluate its effectiveness. As you say, directly benefitting from the spread itself.
I guess what I’m asking about is the effectiveness in practice of ECMs on low frequency timeframes. Consistently, I’m told it isn’t, though I don’t personally know.
 
sorry guys just one more question: if my original time series is stationary, but then i transform the original time series to a Moving Average of span 2 and then run an AR(1) model on the transformed series, would this be ok even if the transformed time series is now not stationary? thanks!
 
It would be better to get some context, but here are some thoughts:
When you say moving average of span 2, do you mean MA(2) or do you mean just a two-period moving average of the original series? If you mean MA(2), then you could just instead estimate ARMA(1,2). If you mean a two-period moving average, then I’m not sure what the purpose would be of running the AR(1) since by definition the value in t will be correlated with the value in t-1.
 
thanks jmh, i mean a two period moving average. basically im still trying to estimate mean reversion. so i computed weekly averages of my rates and im still getting mean reversino estimates that are way off from the expected range. however, if i smooth the data with this two period moving average, the results look much better. but i guess what you’re saying is it doesn’t really make sense to run an AR(1) model on this?
 
jimjohn Wrote:
——————————————————-
> sorry guys just one more question: if my original
> time series is stationary, but then i transform
> the original time series to a Moving Average of
> span 2 and then run an AR(1) model on the
> transformed series, would this be ok even if the
> transformed time series is now not stationary?
> thanks!
If the original series is stationary, then its moving average is also stationary.
 
bchadwick Wrote:
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> OK, now that I’ve written all that, I guess I can
> answer better:
>
> I’m more comfortable doing analysis to improve
> forecasting myself, but ultimately we’re in this
> business to make money, and so one has to make the
> trade or construct the portfolio.
>
> But my instinct, when using these kinds of models
> is to use a shrinkage factor, chosen subjectively,
> to account for the fact that these models always
> look more certain than they really are.
It’s always wise to be cautious of over-curvefitting. Your approach seems practical.
 
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